Optimum Nonlinear Correntropy Filted

ABSTRACT

A signal processing method is provided. The method includes receiving a signal input, and filtering the signal input using a nonlinear correntropy filter. The method additionally includes generating an output based upon the filtering of the signal input. The nonlinear correntropy filter can be configured as a nonlinear Wiener filter. Alternatively, the nonlinear correntropy filter can be configured as a correntropy least mean square (LMS) filter, or a correntropy Newton/LMS filter.

FIELD OF THE INVENTION

The present invention is related to the field of signal processing, and,more particularly, to statistical-based signal detection and estimation.

BACKGROUND OF THE INVENTION

A filter, or estimator, typically refers to a system that is designed toextract information from a signal affected by or otherwise corruptedwith noise. Accordingly, a filter is intended to extract information ofinterest from noisy data. Filter, or estimation, theory has been appliedin a wide variety of fields, including communications, radar, sonar,navigation, seismology, finance, and biomedical engineering.

The Wiener filter, which remains one of the outstanding achievements of20th Century optimal system design, optimally filters a signal. Thefiltering or estimation effected with the Wiener filter is optimal inthe statistical sense of minimizing the average squared error betweenthe desired and the actual output of a system. The Wiener filter extendsthe well-known solution of regression to linear functional spaces; thatis, the space of functions of time, or Hilbert Space.

The manner in which Wiener filters are typically applied in digitalsystems and computers is in an L-dimensional linear vector space(R^(L)). This is due to the fact that the filter topology normallyutilized in this context is a finite-duration impulse response (FIR)filter. Given an input signal x(n), considered to be stationary randomprocess, and a desired response d(n), also a stationary random process,the best linear filter of order L for approximating the desired responsed(n) in the mean square error sense is a FIR filter. The FIR filter is aweight vector w=R⁻¹p, where R is the autocorrelation matrix of the inputsignal and p is the crosscorrelation vector between the input signalx(n) and the desired response d(n).

Due to the properties of the autocorrelation function of real or complexstationary random processes, the weight vector w can be computed with analgorithmic complexity of O(L²). Alternatively, search procedures basedon the least mean square (LMS) algorithm can find the optimal weightvector in O(L) time. Due to the power of the solution and its relativelystraightforward implementation, Wiener filters have been extensivelyutilized in most, if not all, areas of electrical engineering.

There are three basic types of estimation problems: (1) filtering, whichinvolves the extraction of information in real time (i.e., using datauntil time n); (2) smoothing, according to which the extraction is doneat time n₁<n, where n represents the present time; and (3) prediction,according to which the extraction of information is done at a time orsample n₂>n. The Wiener filter is the optimal linear estimator for eachone of these estimation problems.

There are four general classes of applications for Wiener filters: (1)identification, in which the input and desired response for the Wienerfilter come from the input and output of an unknown plant (man-made orphysical and biological systems); (2) inverse modeling, in which theinput and desired response of the Wiener filter come respectively fromthe output of the plant and from its input (eventually with a delayincluded): (3) prediction, in which the input and desired responses tothe Wiener filter are given respectively by the delayed version of thetime series and the current sample; and (4) interference cancellation,in which the input and desired responses for the Wiener filter comerespectively from the reference signal (signal+noise) and primary input(noise alone).

Wiener filters have also been applied in the context ofmultiple-input—single-input (MISO) systems and devices, such asbeamformers, whereby several antennas are used to capture parts of thesignal, and the objective is to optimally combine them. Additionally,Wiener filters have been applied in the context ofmultiple-input—multiple-output (MIMO) systems and devices, whereby thegoal is to optimally estimate the best projection of the input toachieve simultaneous multiple desired responses. The engineering areaswhere Wiener filers have been applied include communication systems(e.g., channel estimation and equalization, and beam forming), optimalcontrols (e.g., system identification and state estimation), and signalprocessing (e.g., model-based spectral analysis, and speech and imageprocessing). Not surprisingly, Wiener filters are one of the centralpillars of optimal signal processing theory and applications.

Despite their wide-spread use, Wiener filters are solutions limited tolinear vector spaces. Numerous attempts have been made to createnonlinear solutions to the Wiener filter, based in the main on Volterraseries approximation. Unfortunately, though, these nonlinear solutionsare typically complex and usually involve numerous coefficients. Thereare also two types of nonlinear models that have been commonly used: TheHammerstein and the Wiener models. The Hammerstein and Wiener models arecharacterized by static nonlinearity and composed of a linear system,where the linear system is adapted using the Wiener solution. However,the choice of the nonlinearity is critical to achieving adequateperformance, because it is a linear solution that is obtained in thetransformed space according to these conventional techniques.

Recent advances in nonlinear signal processing have used nonlinearfilters, commonly known as dynamic neural networks or fuzzy systems.Dynamic neural networks have been extensively used in the same basicapplications of Wiener filters when the system under study is nonlinear.However, there typically are no analytical solutions to obtain theparameters of neural networks. They are normally trained using thebackpropagation algorithm or its modifications (backpropagation throughtime (BPTT) or real-time recurrent learning (RTRL), as well as globalsearch methods such as genetic algorithms or simulated annealing.

In some other cases, a nonlinear transformation of the input is firstimplemented and a regression is computed at the output. A good exampleof this is the radial basis function (RBF) network and more recently thekernel methods. The disadvantage of these alternate techniques ofprojection is the tremendous amount of computation required, which makesthem impractical for most real world cases. For instance, to implementkernel regression on a 1,000-point sample, a 1,000×1,000 signal matrixhas to be solved. By comparison, if a linear Wiener filter of order 10is to be computed, only a 10×10 matrix is necessary.

Accordingly, there is a need to extend the solutions for the Wienerfilter beyond solutions in linear vector spaces. In particular, there isa need for a computationally efficient and effective mechanism forcreating nonlinear solutions to the Wiener filter.

SUMMARY OF THE INVENTION

The present invention provides a nonlinear correntropy filter that canextent filter solutions, such as the those for the Wiener filter, beyondsolutions in linear vector spaces. Indeed, the present invention canprovide an optimal nonlinear correntropy filter.

Moreover, the invention can provide iterative solutions to a correntropyWiener filter, which can be obtained using a least mean square and/orrecursive least square algorithm using correntropy. The variousprocedures can provide optimum nonlinear filter solutions, which can beapplied online.

One embodiment of the invention is signal processing method. The methodcan include receiving a signal input and filtering the signal inputusing a nonlinear correntropy filter. The method further can includegenerating an output based upon the filtering of the signal input. Moreparticularly, the nonlinear correntropy filter can comprise a nonlinearWiener filter, a correntropy least mean square (LMS) filter, or acorrentropy Newton/LMS filter.

Another embodiment of the invention is a nonlinear filter. The nonlinearfilter can include a signal input that receives a signal input from anexternal signal source. Additionally, the nonlinear filter can include aprocessing unit that generates a filtered signal output by filtering thesignal input using a nonlinear Wiener filter, a correntropy least meansquare (LMS) filter, or a correntropy Newton/LMS filter.

Still another embodiment of the invention is a method of constructing anonlinear correntropy filter. The method can include generating acorrentropy statistic based on a kernel function that obeyspredetermined Mercer conditions. The method further can includedetermining a plurality of filter weights based upon the correntropystatistic computed. The plurality of filter weights, moreover, can becomputed based on an inverse correntropy matrix, correntropy least meansquare (LMS) algorithm or correntropy LMS/Newton algorithm.

BRIEF DESCRIPTION OF THE DRAWINGS

There are shown in the drawings, embodiments which are presentlypreferred, it being understood, however, that the invention is notlimited to the precise arrangements and instrumentalities shown.

FIG. 1 is a schematic view of a correntropy filter, according to oneembodiment of the invention.

FIG. 2 is a schematic view of an application of a correntropy filter,according to another embodiment of the invention.

FIG. 3 is a schematic view of an application of a correntropy filter,according to yet another embodiment of the invention.

FIG. 4 is a schematic view of an application of a correntropy filter,according to still another embodiment of the invention.

FIG. 5 is a schematic view of an application of a correntropy filter,according to yet another embodiment of the invention.

FIG. 6 is a flowchart of the exemplary steps of a method of a processinga signal based on nonlinear correntropy-based filtering, according tostill another embodiment of the invention.

FIG. 7 is a flowchart of the exemplary steps of a method 700 forconstructing a nonlinear correntropy filter, according to yet anotherembodiment of the invention.

DETAILED DESCRIPTION

The correntropy of the random process x(t) at instances t₁ and t₂ isdefined as

V(t ₁ ,t ₂)=E(k(x _(t) ₁ −˜x _(t) ₂ )),   (1)

where E[·] is the expected value operator, and k, a is kernel functionthat obeys the Mercer conditions. The kernel function, k, can be, forexample, the Gaussian function:

$\begin{matrix}{{k(x)} = {\frac{1}{\sqrt{2\pi}\sigma}{^{- {(\frac{x - \overset{\_}{x}}{\sqrt{2}\sigma})}^{2}}.}}} & (2)\end{matrix}$

It will be apparent from the discussion herein that other functions canbe used in lieu of the Gaussian function of equation (2). Indeed, thecorrentropy defined in equation (1) can be based on any other kernelfunction obeying the Mercer conditions as well. As will be readilyappreciated by one of ordinary skill in the art, in accordance with theMercer conditions k is both symmetric and positive definite.

The correntropy is a positive function that defines a unique reproducingkernel Hilbert space that is especially appropriate for statisticalsignal processing. According to one aspect of the invention, the samplesxi of an input time series are mapped to a nonlinear space by φ(x_(i)),where k(x_(i),x_(j))=<φ(x₁),φ((x_(j))>, the brackets denoting the innerproduct operation. When the Gaussian kernel is utilized, the inputsignal x(t) is transformed to the surface of a sphere of radius

$\frac{1}{\sigma \sqrt{2\pi}}$

in kernel space. Therefore, correntropy estimates the average cosine ofthe angle between two points separated by a lag on the sphere.

Correntropy for discrete, strictly stationary and ergodic randomprocesses can be estimated as

$\begin{matrix}{{\hat{V}(m)} = {\frac{1}{N - m + 1}{\sum\limits_{i = 0}^{N}{{k\left( {x_{i} - x_{i - m}} \right)}.}}}} & (3)\end{matrix}$

The relationship with information theoretic learning is apparent fromthe following. The mean of the correntropy estimate of a random processx_(k) over the lag is

${{\hat{V}\lbrack m\rbrack}} = {\frac{1}{{2N} - 1}{\sum\limits_{m = {{- N} + 1}}^{N - 1}{\frac{1}{N - {m}}{\sum\limits_{n = m}^{N - 1}{{\kappa \left( {x_{n} - x_{n - m}} \right)}.}}}}}$

which equals the entropy of the random variable x estimated with Parzenwindows

${V = {{\int_{- \infty}^{\infty}{\left( {\frac{1}{N}{\sum\limits_{n = 0}^{N - 1}{\kappa \left( {x - x_{n}} \right)}}} \right)^{2}\ {x}}} = {\frac{1}{N^{2}}{\sum\limits_{n^{\prime} = 0}^{N - 1}{\sum\limits_{n = 0}^{N - 1}{\kappa^{\prime}\left( {x_{n} - x_{n}^{\prime}} \right)}}}}}},$

where, as will be readily understood by one of ordinary skill in theart, entropy provides a measure of randomness, and the Parzen windowscorrespond to methods of estimating the probability density function ofa random variable.

Wiener Filter Based On Correntropy

Another aspect of the invention is a nonlinear Wiener filter based onthe correntropy function already described. According to this aspect ofthe invention, for an input φ(x(n)) to a Wiener structure, (L+1) beingthe order of the filter and φ being a function defined such thatE[k(x_(i)−x_(j))]=E[φ(x_(i)), φ(x_(j))], the following composite vectoris generated using L lags of φ(x(n)):

$\begin{matrix}{{\Phi (n)} = \begin{bmatrix}{\varphi \left( {x(n)} \right)} \\{\varphi \left( {x\left( {n - 1} \right)} \right)} \\\vdots \\{\varphi \left( {x\left( {n - L} \right)} \right)}\end{bmatrix}} & (4)\end{matrix}$

The (L+1) filter weights are given by the following vector:

$\begin{matrix}{\Omega = \begin{bmatrix}\omega_{0} \\\omega_{1} \\\vdots \\\omega_{L}\end{bmatrix}} & (5)\end{matrix}$

According to this formulation, the output is

$\begin{matrix}{{y(n)} = {{\Omega^{T}{\Phi (n)}} = {\sum\limits_{i = 0}^{L}{\omega_{i}{\varphi \left( {x\left( {n - i} \right)} \right)}}}}} & (6)\end{matrix}$

The configuration of the filter, according to this aspect of theinvention, follows from the following formulation of the optimizationproblem: Minimize the mean square error, E{y(n)−d(n)}² with respect toΩ. Initially,

E{y(n) − d(n)}² = E{Ω^(T)Φ(n) − d(n)}^(2^(*))

The optimization solution is determined as follows:

$\begin{matrix}{\frac{\partial\left( {E\left\{ {{{\Phi (n)}^{T}\Omega} - {d(n)}} \right\}^{2}} \right)}{\partial\Omega} = {\left. 0\Rightarrow{E\left\{ {- {2\left\lbrack {{\Phi (n)}\left( {{{\Phi (n)}^{T}\Omega} - {d(n)}} \right)} \right\rbrack}} \right\}} \right. = {\left. 0\Rightarrow{E\left\{ {{\Phi (n)}{\Phi (n)}^{T}} \right\} \Omega} \right. = {E\left\{ {{d(n)}{\Phi (n)}} \right\}}}}} & (7) \\{{V\; \Omega} = {E\left\{ {{d(n)}{\Phi (n)}} \right\}}} & (8)\end{matrix}$

where, V is the correntropy matrix whose ij^(th) element, for i,j=1,2, .. . , L+1, is

E{K(x(n−i+1), x(n−j+1))}.

Moreover, assuming ergodicity, the expected value E{.} can beapproximated by the time average. Accordingly,

$\begin{matrix}{{\Omega = {V^{- 1}\frac{1}{N}{\sum\limits_{k = 1}^{N}{{d(k)}{\Phi (k)}}}}},} & (9)\end{matrix}$

where V⁻¹ represents the inverse of the correntropy matrix and N is thenumber of samples in the window of calculation. The output, therefore,is

$\begin{matrix}\begin{matrix}{{y(n)} = {{\Phi^{T}(n)}\Omega}} \\{= {{\Phi^{T}(n)}V^{- 1}\frac{1}{N}{\sum\limits_{k = 1}^{N}{{d(k)}{\Phi (k)}}}}} \\{= {\frac{1}{N}{\sum\limits_{k = 1}^{N}{\sum\limits_{j = 0}^{L}{\sum\limits_{i = 0}^{L}{{\varphi \left( {n - i} \right)}a_{ij}{\varphi \left( {k - j} \right)}{d(k)}}}}}}} \\{= {\frac{1}{N}{\sum\limits_{k = 1}^{N}{{d(k)}{\sum\limits_{j = 0}^{L}{\sum\limits_{i = 0}^{L}{a_{ij}\left\{ {{\varphi \left( {n - i} \right)}{\varphi \left( {k - j} \right)}} \right\}}}}}}}} \\{= {\frac{1}{N}{\sum\limits_{k = 1}^{N}{{d(k)}{\sum\limits_{j = 0}^{L}{\sum\limits_{i = 0}^{L}{a_{ij}\left\{ {{\varphi \left( {n - i} \right)}{\varphi \left( {k - j} \right)}} \right\}}}}}}}} \\{\cong {\frac{1}{N}{\sum\limits_{k = 1}^{N}\left\{ {{d(k)}{\sum\limits_{j = 0}^{L}{\sum\limits_{i = 0}^{L}{a_{ij}{K\left( {{x\left( {n - i} \right)},{x\left( {k - j} \right)}} \right)}}}}} \right\}}}}\end{matrix} & (10)\end{matrix}$

where a_(ij) is the ij^(th) element of V⁻¹ the final expression isobtained by approximating {φ(n−i)φ(k−j)} by K(x(n−i),x(k−j)), whichholds good on an average sense. Equation 10 shows the calculation thatneeds to be done to compute the Wiener filter based on correntropy.

This solution effectively produces a nonlinear filter in the originalspace due to the mapping to the surface of infinite dimensional sphere,although the solution can still be analytically computed in the tangentbundle of the sphere. This aspect of the invention provides asignificant advance over the conventional Wiener filter.

LMS Filter Based On Correntropy

The least mean squares filter can be derived by using the stochasticversion of cost function (obtained by dropping the expected valueoperator in J(Ω)=E{Ω^(T)Φ(n)−d(n)}² above) resulting inĴ(Ω)={Ω^(T)Φ(n)−d(n)}² and the gradient given by

∇{circumflex over (J)}(Ω)=−e(n)Φ(n),   (11)

where e(n)=d(n)−Ω^(T)Φ(n) is the instantaneous error at time n.

Since the cost function is being minimized, the method of gradientdescent is applied using the stochastic gradient (11). Thus the updatedweight at each instant n is given by,

Ω_(n)=Ω_(n−1) +ηe(n)Φ(n).   (12)

From (12) it follows that Ω_(N) is related to the initialization Ω₀ suchthat

$\begin{matrix}{\Omega_{n} = {\Omega_{0} + {\eta {\sum\limits_{i = 1}^{n - 1}{{e(i)}{{\Phi (i)}.}}}}}} & (13)\end{matrix}$

With Ω₀=0, the output at n is given by

$\begin{matrix}\begin{matrix}{{y(n)} = {\Omega_{n}^{T}{\Phi (n)}}} \\{= {\eta {\sum\limits_{i = 1}^{n - 1}{{e(i)}\left\{ {{\Phi (i)}^{T}{\Phi (n)}} \right\}}}}} \\{= {\eta {\sum\limits_{i = 1}^{n - 1}{{e(i)}{\sum\limits_{k = 0}^{L}\left\{ {{\varphi \left( {i - k} \right)}^{T}{\varphi \left( {n - k} \right)}} \right\}}}}}} \\{{= {\eta {\sum\limits_{i = 1}^{n - 1}{{e(i)}{\sum\limits_{k = 0}^{L}{K\left( {{x\left( {i - k} \right)},{x\left( {n - k} \right)}} \right)}}}}}},}\end{matrix} & (14)\end{matrix}$

where

$\sum\limits_{k = 0}^{L}\left\{ {{\varphi \left( {i - k} \right)}^{T}{\varphi \left( {n - k} \right)}} \right\}$

is approximated by

$\sum\limits_{k = 0}^{L}{{K\left( {{x\left( {i - k} \right)},{x\left( {n - k} \right)}} \right)}.}$

It further follows from linear filtering theory that (14), which givesthe correntropy least mean square (CLMS) filter, converges to minimizethe original mean square error. According to the invention, the stepsize is chosen according the trade-off between the speed of convergenceand the final excess mean square error or mis-adjustment. The solution(14) does not require regularization as employed in conventional methodsthat use kernels. Accordingly, the procedure provides a solution that isautomatically regularized by the process of using the previous errors toestimate the next output.

Newton/LMS Filter Based On Correntropy

A better trade-off of misadjustment versus speed of convergence can beobtained at the expense of extra computation by incorporating acovariance matrix in the formulation (14). This results in

$\begin{matrix}{{{y(n)} = {\eta {\sum\limits_{i = 1}^{n - 1}{{e(i)}{\sum\limits_{j = 0}^{L}{\sum\limits_{k = 0}^{L}{a_{ij}{K\left( {{x\left( {i - k} \right)},{x\left( {n - j} \right)}} \right)}}}}}}}},} & (15)\end{matrix}$

where a_(ij) is the ij^(th) element of V⁻¹. The solution (15) is termedthe correntropy Newton/LMS (CN/LMS)filter.

It is to be noted at this juncture that the above-described techniquesintroduce an extra user-determined free parameter. The extra parameterto be determined by the user is the size of the Gaussian kernel that isused in the transformation to the sphere. It effectively controls thecurvature of the infinitely dimensional sphere, and it affects theperformance. There are three ways to set this free parameter: (1) givenknowledge of the signal statistics, the user can apply Silverman's rule,known to those of ordinary skill in the art, applying the rule to setthe kernel size as in a density estimation; (2) the user can employmaximum likelihood estimation in the joint space; or (3) the user canadaptively determine the free parameter using the LMS algorithm. Anotherdegree of freedom is the choice of the kernel function. Although thisinvention does not specify the mechanisms of its choice, the mathematicsof Mercer Theorem provide an inclusion of the invention to any suchkernels.

FIG. 1 is a schematic illustration of a nonlinear correntropy-basedfilter 100, according to one embodiment of the invention. The nonlinearcorrentropy-based filter 100 illustratively comprises a signalpreprocessor 102 for receiving a signal input, and a processing unit 104for linear filtering the processed signal input. Note that the functionφ(x) is implicitly defined by E[k(x_(i)−x_(j))]=E[φ(x_(i)), φ(x_(j))]and both blocks have to be computed in tandem as stated above. Thenonlinear correntropy-based filter 100 can be implemented in dedicatedhardwired circuitry. Alternatively, the nonlinear correntropy-basedfilter 100 can be implemented in machine-readable code configured to runon a general-purpose or application-specific computing device comprisinglogic-based circuitry. According to yet another embodiment, thenonlinear filter 100 can be implemented in a combination of hardwiredcircuitry and machine-readable code.

According to a particular embodiment, the correntropy filter outputy(n)=Φ_(T)(n)Ω is computed by averaging over the data set the product ofthe desired signal samples with the Gaussian kernel of the input at thedefined lags and weighted by the corresponding entries of the inverse ofthe correntropy matrix, as explained in equation 11.

Still referring to FIG. 1, the nonlinear correntropy-based filter 100,in one application, extends the Wiener filter in context of thestatistical filtering problem. In particular, the filtered signal outputy(n) generated by the nonlinear correntropy-based filter 100 isoptionally supplied to a summer 106, to which a desired response d(n) isalso supplied. The difference between the desired response d(n) and thefiltered signal output y(n) provides an estimation error.

A particular application of the nonlinear correntropy-based filter isidentification of a model representing an unknown plant. A system 200for determining an identification is schematically illustrated in FIG.2. The system 200 provides a model that represents the best fit,according to a predefined criterion, to an unknown plant. The system 200comprises a nonlinear correntropy-based filter 202 and a plant 204 thatis to be identified. Both the nonlinear correntropy-based filter 202 andthe plant 204 are driven by the same input to the system 200. Thefiltered output generated by the nonlinear correntropy-based filter 202,based on the input, is supplied to a summer 206 along with the plantresponse to the same system input. The summer 206 generates an errorbased on the difference between the filtered output and the plantresponse. The nonlinear correntropy-based filter adaptively responds tothe error term, through the illustrated feedback. The supply of systeminput and corresponding adaptation repeat until the best fit isobtained.

Another application of the nonlinear correntropy-based filter is that ofinverse modeling of an unknown “noisy” plant, as will be readilyunderstood by one of ordinary skill in the art. A system 300 forproviding an inverse model is schematically illustrated in FIG. 3. Theinverse model produced represents a best fit, again, according to apredefined criterion, of the unknown noisy plant. The system 300comprises a plant 302 and a delay 304, which each receive the systeminput. Additionally, the system 300 includes a nonlinearcorrentropy-based filter 306 to which the output of the plant 302 issupplied. Based on the plant 302 output, the nonlinear correntropy-basedfilter 306 generates a filtered output.

The filtered output is supplied to a summer 308 along with the systeminput, the latter being delayed by the delay 304 interposed between thesystem input and the summer. The summer 308 generates an error based onthe difference between the filtered output and the delayed system input.The nonlinear correntropy-based filter adaptively responds to theresulting error term, through the illustrated feedback. The supply ofsystem input and corresponding adaptation repeat until the error meets apredefined criterion.

Yet another application of the nonlinear correntropy-based filter isprediction. FIG. 4 provides a schematic illustration of a system 400 forgenerating predictions using the nonlinear correntropy-based filter. Asshown, a random signal is supplied through a delay 402 to the nonlinearcorrentropy filter 404. The random signal is also supplied directly to asummer 406, as is the filtered output generated by the nonlinearcorrentropy-based filter 404. According to this arrangement, thenonlinear correntropy-based filter 404 provides a prediction of thepresent value of the random signal, the prediction being best in termsof a predefined criterion. The present value of the random signalrepresents the desired response of the nonlinear correntropy-basedfilter 404, while past values of the random signal supply inputs.

If the system is used a predictor, then the output of the system (systemoutput 1) is the output of the nonlinear correntropy-based filter 404.If the system is used as a prediction-error filter, then the output ofthe system (system output 2) is the difference between the random signaland the output of the nonlinear correntropy-based filter 404, both ofwhich are supplied to the summer 406.

Still another application of the nonlinear correntropy-based filter isinterference cancellation. A system 500 using a nonlinearcorrentropy-based filter 502 is schematically illustrated in FIG. 5. Aprimary signal is supplied to a summer 502, as is the output of thenonlinear correntropy-based filter 502 in the system 500. The primarysignal is the desired response for the nonlinear correntropy filter 502.The output of the nonlinear correntropy-based filter 502 is based on areference signal input. The reference signal can be derived from one ormore sensors, which are positioned such that the information-bearingsignal component is weak or otherwise difficult to determine. The system500 is used to cancel unknown interference in the primary signal so asto enhance detection of the information content. The cancellationafforded by the nonlinear correntropy-based filter 502 is optimizedaccording to a predefined criterion.

FIG. 6 is a flowchart of the exemplary steps of a method 600 of signalprocessing, according to still another embodiment of the invention. Themethod includes receiving a signal input at step 602. At step 604, thereceived signal is filtered using a a nonlinear correntropy filter. Themethod continues at step 606 with the generation of an output based uponthe filtering of the signal input. The method illustratively concludesat step 608.

According to one embodiment of the method 600, the step of generating anoutput 606 comprises generating a prediction of a random signal, theprediction being a best prediction based upon a predetermined criterion.Moreover, the prediction can comprise an estimation of an error, wherebythe error is based on a difference between an output generated by asystem in response to the signal input and a predefined desired systemoutput.

According to another embodiment of the method, the step of generating anoutput 606 comprises generating an identification of a nonlinear system.Alternatively, the step of generating an output 606 can comprisegenerating an inverse model representing a best fit to a noisy plant.According to yet another embodiment, the step of generating an output606 comprises generating an inverse model representing a best fit to anoisy plant.

FIG. 7 is a flowchart of the exemplary steps of a method 700 forconstructing a nonlinear correntropy filter, according to yet anotherembodiment of the invention. The method 700 is based on theabove-described equations relating to the determination of a correntropystatistic, V, and the determination of filter weights based on thecorrentropy statistic. The method 700 begins at step 702 with thegeneration of the correntropy statistic based on a kernel function thatobeys the predetermined Mercer conditions, where the correntropystatistic, V is defined to be, as above, V(t₁,t₂)=E(k(x_(t) ₁ −x_(t) ₂)), E[·] being the expected value operator, and k being the kernelfunction that obeys predetermined Mercer conditions. The method 700continues at step 704 with the determination of the above-describedfilter weights, the filter weights being based upon the correntropystatistic as also described above. The method 700 concludes at step 706.

Yet another method aspect of the invention, is a method of generating anonlinear function. The method, more particularly, comprises generatinga correntropy function as already described and computing an expectedvalue of the correntropy function. The method further includesgenerating a nonlinear function for which the expected value of thepairwise product of data evaluations is equal to the expected value ofthe correntropy function.

As noted herein, the invention can be realized in hardware, software, ora combination of hardware and software. The invention, moreover, can berealized in a centralized fashion in one computer system, or in adistributed fashion where different elements are spread across severalinterconnected computer systems. Any kind of computer system or otherapparatus adapted for carrying out the methods described herein issuited. As also noted herein, a typical combination of hardware andsoftware can be a general purpose computer system with a computerprogram that, when being loaded and executed, controls the computersystem such that it carries out the methods described herein.

The invention also can be embedded in a machine-readable storage mediumor other computer-program product, which comprises all the featuresenabling the implementation of the methods described herein, and whichwhen loaded in a computer system is able to carry out these methods.Computer program in the present context means any expression, in anylanguage, code or notation, of a set of instructions intended to cause asystem having an information processing capability to perform aparticular function either directly or after either or both of thefollowing: a) conversion to another language, code or notation; b)reproduction in a different material form.

This invention can be embodied in other forms without departing from thespirit or essential attributes thereof. Accordingly, reference should bemade to the following claims, rather than to the foregoingspecification, as indicating the scope of the invention.

1. A signal processing method, comprising receiving a signal input;filtering the signal input using a nonlinear correntropy filter; andgenerating an output based upon the filtering of the signal input,wherein the nonlinear correntropy filter comprises a nonlinear Wienerfilter, a correntropy least mean square (LMS) filter, or a correntropyNewton/LMS filter.
 2. The method of claim 1, wherein generating anoutput comprises generating a prediction of a random signal, theprediction being a best prediction based upon a predetermined criterion.3. The method of claim 2, wherein the prediction comprises an estimationof an error, the error being based on a difference between an outputgenerated by a system in response to the signal input and a predefineddesired system output.
 4. The method of claim 1, wherein generating anoutput comprises generating an identification of a nonlinear system. 5.The method of claim 1, wherein generating an output comprises generatingan inverse model representing a best fit to a noisy plant.
 6. Anonlinear filter, comprising: a signal input that receives a signalinput from an external signal source; and a processing unit thatgenerates a filtered signal output by filtering the signal input using anonlinear Wiener filter, a correntropy least mean square (LMS) filter,or a correntropy Newton/LMS filter.
 7. The nonlinear filter of claim 6,wherein the signal input comprises a plurality of lagged discretesignals mapped by a correntropy function, and wherein the processingunit is configured to filter the projected signal input by applying aplurality of filter weights in the projected space to the laggeddiscrete signals.
 8. The nonlinear filter of claim 7, wherein each ofthe plurality of filter weights is based on a minimization of anexpected value between the desired output and the filtered signal in theprojected signal space by the correntropy function.
 9. The nonlinearfilter of claim 7, wherein the lagged discrete signals comprise signalscharacterized as ergodic signals, and wherein the plurality of filterweights is computed based on an inverse correntropy matrix, correntropyLMS algorithm or correntropy LMS/Newton algorithm.
 10. A signalprocessing system, comprising a nonlinear correntropy-based filter forgenerating a filter output by filtering a signal; and a summer forcomputing a difference between the filter output and a desired signalresponse.
 11. The system of claim 10, further comprising a plant inelectrical communication with the summer, the plant generating thedesired signal response based upon a system input signal that issupplied to both the nonlinear correntropy-based filter and the plant.12. The system of claim 10, further comprising a plant in electricalcommunication with a nonlinear correntropy-based filter and a delay inelectrical communication with the summer, the plant receiving a systemsignal input and generating in response thereto the signal filtered bythe nonlinear correntropy-based, and the delay receiving the same systemsignal input and in response thereto supplying the desired signal to thesummer.
 13. The system of claim 10, further comprising a delay inelectrical communication with the nonlinear correntropy-based filter tosupply the signal thereto in response to receiving a random signal, therandom signal also defining the desired signal supplied directly to thesummer.
 14. The system of claim 10, wherein the signal defines areference signal, and wherein a primary signal is supplied directly tothe summer.
 15. A machine-readable storage medium, the medium comprisingmachine-executable instructions for: receiving a signal input; filteringthe signal input using a nonlinear correntropy filter; and generating aoutput based upon the filtering of the signal input, wherein thenonlinear correntropy filter comprises a nonlinear Wiener filter, acorrentropy least mean square (LMS) filter, or a correntropy Newton/LMSfilter.
 16. The storage medium of claim 15, wherein generating an outputcomprises generating a prediction of a random signal, the predictionbeing a best prediction based upon a predetermined criterion.
 17. Thestorage medium of claim 16, wherein the prediction comprises anestimation of an error, the error being based on a difference between anoutput generated by a system in response to the signal input and apredefined desired system output.
 18. The storage medium of claim 15,wherein generating an output comprises generating an identification of anonlinear system.
 19. The storage medium of claim 15, wherein generatingan output comprises generating an inverse model representing a best fitto a noisy plant.
 20. A method of constructing a nonlinear correntropyfilter, the method comprising generating a correntropy statistic basedon a kernel function that obeys predetermined Mercer conditions, wherethe correntropy statistic, V is defined as V(t₁,t₂)=E(k(x_(t) ₁ −x_(t) ₂)), E[·] being an expected value operator, and k being the kernelfunction that obeys predetermined Mercer conditions; and determining aplurality of filter weights based upon the correntropy statisticcomputed wherein the plurality of filter weights is computed based on aninverse correntropy matrix, correntropy LMS algorithm or correntropyLMS/Newton algorithm.